TokenMix Research Lab · 2026-03-17

TokenMix API Quickstart: 150+ Models in 5 Minutes (2026)

Getting Started with TokenMix API in 5 Minutes

Last Updated: 2026-04-29
Author: TokenMix Research Lab

Drop-in OpenAI-compatible endpoint at https://api.tokenmix.ai/v1 — change one base_url, keep your existing OpenAI SDK code, gain access to GPT-4o, Claude Sonnet 4, Gemini 2.0 Flash, DeepSeek R1, Llama 4, and 300+ more.

TokenMix gives you access to all major AI models — GPT-4o, Claude Sonnet 4, Gemini 2.0 Flash, DeepSeek R1, Llama 4, and more — through a single OpenAI-compatible API. If you have used the OpenAI SDK before, you already know how to use TokenMix. If you have not, this guide will get you making API calls in under 5 minutes.

Table of Contents

Step 1: Get Your API Key

Sign up, create a key in Dashboard > API Keys, save it once — keys are shown only at creation.

  1. Sign up at tokenmix.ai
  2. Go to Dashboard > API Keys
  3. Click "Create New Key"
  4. Copy and save your key somewhere secure. You will not be able to see it again.

Step 2: Install the SDK

Use the standard OpenAI SDK — TokenMix is fully OpenAI-compatible, no proprietary client required.

Python:

pip install openai

Node.js:

npm install openai

Step 3: Make Your First API Call

Point base_url at TokenMix and call chat.completions.create exactly as you would with OpenAI — same auth, same payload, same response shape.

Python

import openai
import sys

client = openai.OpenAI(
    base_url="https://api.tokenmix.ai/v1",
    api_key="your-tokenmix-api-key"  # Replace with your actual key
)

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Explain what an API gateway is in two sentences."}
        ],
        max_tokens=200,
        temperature=0.7
    )
    print(response.choices[0].message.content)

except openai.AuthenticationError:
    print("Invalid API key. Check your key at tokenmix.ai/dashboard/keys")
    sys.exit(1)
except openai.RateLimitError:
    print("Rate limit reached. Wait a moment and try again.")
    sys.exit(1)
except openai.APIError as e:
    print(f"API error: {e.message}")
    sys.exit(1)

Node.js

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.tokenmix.ai/v1",
  apiKey: "your-tokenmix-api-key", // Replace with your actual key
});

async function main() {
  try {
    const response = await client.chat.completions.create({
      model: "gpt-4o",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "Explain what an API gateway is in two sentences." },
      ],
      max_tokens: 200,
      temperature: 0.7,
    });

    console.log(response.choices[0].message.content);
  } catch (error) {
    if (error instanceof OpenAI.AuthenticationError) {
      console.error("Invalid API key. Check your key at tokenmix.ai/dashboard/keys");
    } else if (error instanceof OpenAI.RateLimitError) {
      console.error("Rate limit reached. Wait a moment and try again.");
    } else {
      console.error("API error:", error.message);
    }
    process.exit(1);
  }
}

main();

cURL

curl https://api.tokenmix.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-tokenmix-api-key" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain what an API gateway is in two sentences."}
    ],
    "max_tokens": 200
  }'

Step 4: Streaming Responses

Pass stream=True to get token-by-token output — required for any chat UI where users wait on the first character. For chat applications or any UI that shows text as it is generated, use streaming:

Python Streaming

import openai

client = openai.OpenAI(
    base_url="https://api.tokenmix.ai/v1",
    api_key="your-tokenmix-api-key"
)

try:
    stream = client.chat.completions.create(
        model="claude-sonnet-4",
        messages=[
            {"role": "user", "content": "Write a short guide on Python type hints."}
        ],
        stream=True
    )

    for chunk in stream:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="", flush=True)

    print()  # Final newline

except openai.APIError as e:
    print(f"\nStream error: {e.message}")

Node.js Streaming

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.tokenmix.ai/v1",
  apiKey: "your-tokenmix-api-key",
});

async function main() {
  const stream = await client.chat.completions.create({
    model: "claude-sonnet-4",
    messages: [
      { role: "user", content: "Write a short guide on Python type hints." },
    ],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
    }
  }
  console.log();
}

main().catch(console.error);

Step 5: Switch Between Models

Switching models is a one-line change — same endpoint, same SDK, same API key, just edit the model parameter. The best part of using TokenMix: switching models is a one-line change. Every model uses the same endpoint, same SDK, same API key:

# Just change the model parameter
response = client.chat.completions.create(
    model="claude-sonnet-4",  # Or: gpt-4o, gemini-2.0-flash, deepseek-r1, llama-4
    messages=[{"role": "user", "content": "Hello!"}]
)

No new SDK, no new API key, no new billing account. This makes it trivial to benchmark models against each other on your own data.

Common Patterns

The three patterns every production codebase needs: timeouts, retry-with-backoff, and env-var-loaded keys.

Setting a Timeout

client = openai.OpenAI(
    base_url="https://api.tokenmix.ai/v1",
    api_key="your-tokenmix-api-key",
    timeout=30.0  # 30 second timeout
)

Retry with Exponential Backoff

import time
import openai

def call_with_retry(client, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(**kwargs)
        except openai.RateLimitError:
            if attempt == max_retries - 1:
                raise
            wait = 2 ** attempt  # 1s, 2s, 4s
            time.sleep(wait)
        except openai.APIError:
            if attempt == max_retries - 1:
                raise
            time.sleep(1)

Using Environment Variables (Recommended)

import os
import openai

client = openai.OpenAI(
    base_url="https://api.tokenmix.ai/v1",
    api_key=os.environ["TOKENMIX_API_KEY"]  # Set in your environment
)

# In your .env or shell profile
export TOKENMIX_API_KEY=sk-your-key-here

Where to Go Next?

After your first call works: explore the Models page for full pricing, monitor usage in Dashboard, and read function-calling/embeddings docs for advanced features.

You now have everything you need to start building with any major AI model through a single API. The entire setup — from sign-up to working code — should take less than 5 minutes.

FAQ

Do I need a separate API key for each model?

No. One TokenMix API key gives access to all 300+ models — GPT-4o, Claude Sonnet 4, Gemini 2.0 Flash, DeepSeek R1, Llama 4, and others. Switching models is a one-line change to the model parameter in your request payload.

Is TokenMix really 100% OpenAI SDK compatible?

Yes for chat completions, streaming, function calling, embeddings, and image generation. Edge cases: provider-specific features like Anthropic's system prompt caching or Gemini's safety settings need provider-native parameters, but core OpenAI SDK code works unchanged. The base URL change and key swap are the only required edits.

What's the rate limit on new accounts?

New accounts start at 60 requests/minute and 100K tokens/minute, which is enough for development and small production traffic. Rate limits scale with usage history — most teams reach 600 RPM within the first week of regular traffic. Email support if you need higher limits up front for a known launch.

How do I switch from OpenAI directly to TokenMix without rewriting code?

Change two things: set base_url="https://api.tokenmix.ai/v1" and use your TokenMix API key. That's it. The OpenAI SDK code, response parsing, error handling, retries, and streaming all work identically. Test by running your existing code with the new client side-by-side against the OpenAI client.

Are there any models that don't work through the OpenAI-compatible endpoint?

Image generation models use a slightly different request format (closer to OpenAI's images.generate). Vision and audio inputs follow the standard OpenAI multimodal message format. Everything else — text generation, embeddings, function calling — is identical in shape to OpenAI's SDK.

How does billing work when I use multiple models in one app?

You see one consolidated bill across all providers, with per-model breakdowns in the dashboard. Pricing matches each provider's official per-token rate with no markup. Top up credits via Alipay, WeChat Pay, or Stripe — no monthly commitment, pay-as-you-go.

What's the latency overhead of routing through TokenMix vs direct provider calls?

Typically 30-80ms of added TTFT, depending on routing region. For most production workloads this is invisible. If you need absolute minimum latency to a single provider, direct API access is marginally faster — the value of a gateway like TokenMix is multi-model flexibility, consolidated billing, and one-line model swaps, not latency reduction.